Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research

Introduction: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics). Methods and objectives: APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions. Conclusion: APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions.ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models’ prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR.ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Cannabinoids in the management of spasticity associated with multiple sclerosis

Anna Maria Malfitano, Maria Chiara Proto, Maurizio BifulcoDipartimento di Scienze Farmaceutiche, Università degli Studi di SalernoAbstract: The endocannabinoid system and cannabinoid-based treatments have been involved in a wide number of diseases. In particular, several studies suggest that cannabinoids and endocannabinoids may have a key role in the pathogenesis and therapy of multiple sclerosis (MS). In this study we highlight the main findings reported in literature about the relevance of cannabinoid drugs in the management and treatment of MS. An increasing body of evidence suggests that cannabinoids have beneficial effects on the symptoms of MS, including spasticity and pain. In this report we focus on the effects of cannabinoids in the relief of spasticity describing the main findings in vivo, in the mouse experimental allergic encephalomyelitis model of MS. We report on the current treatments used to control MS symptoms and the most recent clinical studies based on cannabinoid treatments, although long-term studies are required to establish whether cannabinoids may have a role beyond symptom amelioration in MS.Keywords: cannabinoids, multiple sclerosis, spasticit

Modified Adenosines Sensitize Glioblastoma Cells to Temozolomide by Affecting DNA Methyltransferases

Glioblastoma (GBM) is the most common and lethal primary malignant brain tumor, and due to its unique features, its management is certainly one of the most challenging ones among all cancers. N6-isopentenyladenosine (IPA) and its analog N6-benzyladenosine (N6-BA) are modified nucleosides endowed with potent antitumor activity on different types of human cancers, including GBM. Corroborating our previous finding, we demonstrated that IPA and N6-BA affect GBM cell line proliferation by modulating the expression of the F-box WD repeat domain-containing-7 (FBXW7), a tumor suppressor with a crucial role in the turnover of many proteins, such as SREBPs and Mcl1, involved in malignant progression and chemoresistance. Luciferase assay revealed that IPA-mediated upregulation of FBXW7 translates in transcriptional inactivation of its oncogenic substrates (Myc, NFkB, or HIF-1α). Moreover, downregulating MGMT expression, IPA strongly enhances the killing effect of temozolomide (TMZ), producing a favorable sensitizing effect starting from a concentration range much lower than TMZ EC50. Through DNA methyltransferase (DNMT) activity assay, analysis of the global DNA methylation, and the histone modification profiles, we demonstrated that the modified adenosines behave similar to 5-AZA-dC, known DNMT inhibitor. Overall, our results provide new perspectives for the first time, suggesting the modified adenosines as epigenetic tools able to improve chemo- and radiotherapy efficacy in glioblastoma and potentially other cancers

In Vitro Evidence of Statins' Protective Role against COVID-19 Hallmarks

Despite the progressions in COVID-19 understanding, the optimization of patient-specific therapies remains a challenge. Statins, the most widely prescribed lipid-lowering drugs, received considerable attention due to their pleiotropic effects, encompassing lipid metabolism control and immunomodulatory and anti-thrombotic effects. In COVID-19 patients, statins improve clinical outcomes, reducing Intensive Care Unit admission, the onset of ARDS, and in-hospital death. However, the safety of statins in COVID-19 patients has been debated, mainly for statins' ability to induce the expression of the ACE2 receptor, the main entry route of SARS-CoV-2. Unfortunately, the dynamic of statins' mechanism in COVID-19 disease and prevention remains elusive. Using different in vitro models expressing different levels of ACE2 receptor, we investigated the role of lipophilic and hydrophilic statins on ACE2 receptor expression and subcellular localization. We demonstrated that the statin-mediated increase of ACE2 receptor expression does not necessarily coincide with its localization in lipid rafts domains, particularly after treatments with the lipophilic atorvastatin that disrupt lipid rafts' integrity. Through a proteomic array, we analyzed the cytokine patterns demonstrating that statins inhibit the release of cytokines and factors involved in mild to severe COVID-19 cases. The results obtained provide additional information to dissect the mechanism underlying the protective effects of statin use in COVID-19

The Endocannabinoid System: A Target for Cancer Treatment

In recent years, the endocannabinoid system has received great interest as a potential therapeutic target in numerous pathological conditions. Cannabinoids have shown an anticancer potential by modulating several pathways involved in cell growth, differentiation, migration, and angiogenesis. However, the therapeutic efficacy of cannabinoids is limited to the treatment of chemotherapy-induced symptoms or cancer pain, but their use as anticancer drugs in chemotherapeutic protocols requires further investigation. In this paper, we reviewed the role of cannabinoids in the modulation of signaling mechanisms implicated in tumor progression

The endocannabinoid system: A target for cancer treatment

n recent years, the endocannabinoid system has received great interest as a potential therapeutic target in numerous pathological conditions. Cannabinoids have shown an anticancer potential by modulating several pathways involved in cell growth, differentiation, migration, and angiogenesis. However, the therapeutic efficacy of cannabinoids is limited to the treatment of chemotherapy-induced symptoms or cancer pain, but their use as anticancer drugs in chemotherapeutic protocols requires further investigation. In this paper, we reviewed the role of cannabinoids in the modulation of signaling mechanisms implicated in tumor progression